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Machine learning for identification of brain activity patterns with applications in gentle touch processing


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Title: Machine learning for identification of brain activity patterns with applications in gentle touch processing
Authors: Björnsdotter Åberg, Malin
E-mail: malin.aberg@neuro.gu.se
Issue Date: 23-Oct-2009
University: University of Gothenburg. Sahlgrenska Academy
Institution: Institute of Neuroscience and Physiology. Department of Physiology
Parts of work: I. Malin Björnsdotter, Karin Rylander and Johan Wessberg. A Monte Carlo method for locally-multivariate brain mapping. Submitted manuscript.

II. Malin Björnsdotter Åberg and Johan Wessberg. An evolutionary approach to the identification of informative voxel clusters for brain state discrimination. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(6), pp. 919-28.
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III. Malin Björnsdotter, Karin Rylander, Johan Wessberg and Håkan Olausson. Separate neural systems underpin discriminative and affective touch in humans. Manuscript.

IV. Malin Björnsdotter, Line Löken, Håkan Olausson, Åke Vallbo and Johan Wessberg. Somatotopic organization of gentle touch processing in the posterior insular cortex. Journal of Neuroscience, 2009, 29(29), pp. 9314-20.
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Date of Defence: 2009-10-23
Disputation: Fredagen den 23 oktober 2009, kl. 13.00, Hörsal Ivan Östholm, Medicinaregatan 13, Göteborg
Degree: Doctor of Philosophy (Medicine)
Publication type: Doctoral thesis
Keywords: Somatosensory
Machine learning
Pattern recognition
fMRI
Support vector machines
Neuroscience
Brain
BOLD
Signal processing
Artificial intelligence
Touch
Human
Unmyelinated
Sensory
Affective
Abstract: Since the first mention of artificial intelligence in the 1950s, the field of machine learning has provided increasingly appealing tools for recognition of otherwise unintelligible pattern representations in complex data structures. Human brain activity, acquired using functional magnetic resonance imaging (fMRI), is a prime example of such complex data where the utility of pattern recognition has been demonstrated in a wide range of studies recently (Haynes et al., Nature Reviews Neuroscience, ... more
ISBN: 978-91-628-7854-2
URI: http://hdl.handle.net/2077/20808
Appears in Collections:Doctoral Theses from Sahlgrenska Academy
Doctoral Theses / Doktorsavhandlingar Institutionen för neurovetenskap och fysiologi
Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet

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